{"id":12486,"date":"2024-05-21T18:26:24","date_gmt":"2024-05-21T18:26:24","guid":{"rendered":"https:\/\/dailyai.com\/?p=12486"},"modified":"2024-05-21T20:56:24","modified_gmt":"2024-05-21T20:56:24","slug":"researchers-train-model-to-create-images-without-seeing-copyrighted-work","status":"publish","type":"post","link":"https:\/\/dailyai.com\/fr\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/","title":{"rendered":"Des chercheurs entra\u00eenent un mod\u00e8le \u00e0 cr\u00e9er des images sans \"voir\" les \u0153uvres prot\u00e9g\u00e9es par le droit d'auteur"},"content":{"rendered":"

Des chercheurs de l'universit\u00e9 du Texas \u00e0 Austin ont mis au point un cadre innovant pour l'entra\u00eenement de mod\u00e8les d'intelligence artificielle sur des images fortement corrompues.\u00a0<\/b><\/p>\n

Connue sous le nom de diffusion ambiante, cette m\u00e9thode permet aux mod\u00e8les d'IA de \"s'inspirer\" des images. sans<\/em> en les copiant directement.<\/span><\/p>\n

Les mod\u00e8les conventionnels de conversion de texte en image utilis\u00e9s par DALL-E<\/span><\/span><\/span>Midjourney et Stable Diffusion risquent d'enfreindre le droit d'auteur parce qu'ils sont form\u00e9s sur des ensembles de donn\u00e9es comprenant des images prot\u00e9g\u00e9es par le droit d'auteur, ce qui les am\u00e8ne parfois \u00e0 reproduire ces images par inadvertance.\u00a0<\/span><\/p>\n

La diffusion ambiante renverse la situation en formant des mod\u00e8les avec des donn\u00e9es d\u00e9lib\u00e9r\u00e9ment corrompues.<\/p>\n

Dans le cadre de la \u00e9tude<\/a>L'\u00e9quipe de recherche, compos\u00e9e d'Alex Dimakis et de Giannis Daras du d\u00e9partement de g\u00e9nie \u00e9lectrique et informatique de l'UT Austin et de Constantinos Daskalakis du d\u00e9partement de g\u00e9nie \u00e9lectrique et informatique de l MIT<\/span><\/span><\/span><\/span><\/span>a entra\u00een\u00e9 un mod\u00e8le Stable Diffusion XL sur un ensemble de donn\u00e9es de 3 000 images de c\u00e9l\u00e9brit\u00e9s.\u00a0<\/span><\/p>\n

Dans un premier temps, on a observ\u00e9 que les mod\u00e8les form\u00e9s sur des donn\u00e9es propres copiaient de mani\u00e8re flagrante les exemples d'apprentissage.\u00a0<\/span><\/p>\n

Toutefois, lorsque les donn\u00e9es d'apprentissage ont \u00e9t\u00e9 corrompues - en masquant de mani\u00e8re al\u00e9atoire jusqu'\u00e0 90% des pixels - le mod\u00e8le a continu\u00e9 \u00e0 produire des images uniques de haute qualit\u00e9.<\/span><\/p>\n

Cela signifie que l'IA n'est jamais expos\u00e9e \u00e0 des versions reconnaissables des images originales, ce qui l'emp\u00eache de les copier.<\/span><\/p>\n

\"Notre cadre permet de contr\u00f4ler le compromis entre la m\u00e9morisation et la performance\". <\/span>a expliqu\u00e9 Giannis Daras<\/span><\/a>un \u00e9tudiant dipl\u00f4m\u00e9 en informatique qui a dirig\u00e9 les travaux.\u00a0<\/span><\/p>\n

\"Au fur et \u00e0 mesure que le niveau de corruption rencontr\u00e9 au cours de la formation augmente, la m\u00e9morisation de l'ensemble de formation diminue.<\/span><\/p>\n

Applications scientifiques et m\u00e9dicales<\/h2>\n

Les utilisations de la diffusion ambiante vont au-del\u00e0 de la r\u00e9solution des probl\u00e8mes de droits d'auteur.\u00a0<\/span><\/p>\n

Selon le professeur Adam Klivans, qui a collabor\u00e9 au projet, \"le cadre pourrait \u00e9galement s'av\u00e9rer utile pour des applications scientifiques et m\u00e9dicales. Cela serait vrai pour pratiquement toutes les recherches pour lesquelles il est co\u00fbteux ou impossible de disposer d'un ensemble complet de donn\u00e9es non corrompues, de l'imagerie des trous noirs \u00e0 certains types d'IRM\".<\/span><\/p>\n

Ceci est particuli\u00e8rement utile dans les domaines o\u00f9 l'acc\u00e8s \u00e0 des donn\u00e9es non corrompues est limit\u00e9, tels que l'astronomie et la physique des particules<\/a>.\u00a0<\/span><\/p>\n

Dans ces domaines et dans d'autres, les donn\u00e9es peuvent \u00eatre extr\u00eamement bruyantes, de mauvaise qualit\u00e9 ou \u00e9parses, ce qui signifie que les donn\u00e9es utiles sont largement surpass\u00e9es en nombre par les donn\u00e9es inutiles. Il serait utile d'apprendre aux mod\u00e8les \u00e0 utiliser plus efficacement des donn\u00e9es sous-optimales.<\/p>\n

Si l'approche de la diffusion ambiante \u00e9tait encore affin\u00e9e, les entreprises d'IA pourraient cr\u00e9er des mod\u00e8les texte-image fonctionnels tout en respectant les droits des cr\u00e9ateurs de contenu original et en \u00e9vitant les probl\u00e8mes juridiques.<\/p>\n

M\u00eame si cela ne r\u00e9soudrait pas les probl\u00e8mes li\u00e9s au fait que les outils d'imagerie artificielle r\u00e9duisent le nombre de travaux pour les artistes r\u00e9els, cela prot\u00e9gerait au moins leurs \u0153uvres d'une reproduction accidentelle dans les produits.<\/p>","protected":false},"excerpt":{"rendered":"

Des chercheurs de l'universit\u00e9 du Texas \u00e0 Austin ont mis au point un cadre innovant pour l'entra\u00eenement de mod\u00e8les d'intelligence artificielle sur des images fortement corrompues. Connue sous le nom d'Ambient Diffusion, cette m\u00e9thode permet aux mod\u00e8les d'IA de \"s'inspirer\" des images sans les copier directement. Les mod\u00e8les classiques de conversion texte-image utilis\u00e9s par DALL-E, Midjourney et Stable Diffusion risquent d'enfreindre le droit d'auteur parce qu'ils sont form\u00e9s sur des ensembles de donn\u00e9es comprenant des images prot\u00e9g\u00e9es par le droit d'auteur, ce qui les am\u00e8ne parfois \u00e0 reproduire ces images par inadvertance. Ambient Diffusion renverse la situation en entra\u00eenant les mod\u00e8les \u00e0 l'aide de donn\u00e9es d\u00e9lib\u00e9r\u00e9ment corrompues. Dans l'\u00e9tude, l'\u00e9quipe de recherche, compos\u00e9e d'Alex Dimakis et de Giannis Daras de l'Universit\u00e9 de l'\u00e9lectricit\u00e9 et de l'informatique (EIA), s'est pench\u00e9e sur la question.<\/p>","protected":false},"author":2,"featured_media":12492,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[165,105],"class_list":["post-12486","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-image-generation","tag-machine-learning"],"yoast_head":"\nResearchers train model to create images without 'seeing' copyrighted work | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/fr\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Researchers train model to create images without 'seeing' copyrighted work | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers at The University of Texas at Austin have developed an innovative framework for training AI models on heavily corrupted images.\u00a0 Known as Ambient Diffusion, this method enables AI models to ‘draw inspiration’ from images without directly copying them. 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In the study, the research team, including Alex Dimakis and Giannis Daras from the Electrical and Computer\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/fr\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-05-21T18:26:24+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-05-21T20:56:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/05\/DALL\u00b7E-2024-05-21-19.21.33-A-high-tech-lab-with-researchers-working-on-a-diffusion-model-for-creating-images-without-seeing-copyrighted-work.-The-focal-point-is-a-large-comput.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1792\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Sam 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